opts <- options(knitr.kable.NA = "")
All EDGE samples excluded
OTU tables rarefied (16S: 9,048 & ITS: 10,116 seqs/sample)
‘Taxa’ are OTUs
Bacterial & Archaeal Dataset:
Bac.AllG
phyloseq-class experiment-level object
otu_table() OTU Table: [ 23156 taxa and 533 samples ]
sample_data() Sample Data: [ 533 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 23156 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.AllG
phyloseq-class experiment-level object
otu_table() OTU Table: [ 6749 taxa and 509 samples ]
sample_data() Sample Data: [ 509 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 6749 taxa by 7 taxonomic ranks ]
Bacterial & Archaeal Dataset:
Bac.ANGE
phyloseq-class experiment-level object
otu_table() OTU Table: [ 14268 taxa and 106 samples ]
sample_data() Sample Data: [ 106 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 14268 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.ANGE
phyloseq-class experiment-level object
otu_table() OTU Table: [ 2985 taxa and 98 samples ]
sample_data() Sample Data: [ 98 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 2985 taxa by 7 taxonomic ranks ]
Bacterial & Archaeal Dataset:
Bac.BOER
phyloseq-class experiment-level object
otu_table() OTU Table: [ 8374 taxa and 84 samples ]
sample_data() Sample Data: [ 84 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 8374 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.BOER
phyloseq-class experiment-level object
otu_table() OTU Table: [ 1662 taxa and 78 samples ]
sample_data() Sample Data: [ 78 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 1662 taxa by 7 taxonomic ranks ]
Bacterial & Archaeal Dataset:
Bac.BOGR
phyloseq-class experiment-level object
otu_table() OTU Table: [ 10108 taxa and 86 samples ]
sample_data() Sample Data: [ 86 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 10108 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.BOGR
phyloseq-class experiment-level object
otu_table() OTU Table: [ 2668 taxa and 92 samples ]
sample_data() Sample Data: [ 92 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 2668 taxa by 7 taxonomic ranks ]
Bacterial & Archaeal Dataset:
Bac.BUDA
phyloseq-class experiment-level object
otu_table() OTU Table: [ 11048 taxa and 83 samples ]
sample_data() Sample Data: [ 83 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 11048 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.BUDA
phyloseq-class experiment-level object
otu_table() OTU Table: [ 2369 taxa and 80 samples ]
sample_data() Sample Data: [ 80 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 2369 taxa by 7 taxonomic ranks ]
Bacterial & Archaeal Dataset:
Bac.SCSC
phyloseq-class experiment-level object
otu_table() OTU Table: [ 17568 taxa and 174 samples ]
sample_data() Sample Data: [ 174 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 17568 taxa by 6 taxonomic ranks ]
Fungal Dataset:
Fun.SCSC
phyloseq-class experiment-level object
otu_table() OTU Table: [ 4347 taxa and 161 samples ]
sample_data() Sample Data: [ 161 samples by 39 sample variables ]
tax_table() Taxonomy Table: [ 4347 taxa by 7 taxonomic ranks ]
- ANGE (Andropogon gerardii / Big bluestem)
- SCSC (Schizachyrium scoparium / Little bluestem)
- BOER (B. eriopoda / Black grama)
- BOGR (B. gracilis / Blue grama)
- BUDA (Bouteloua dactyloides / Buffalograss)
knitr::kable((as.data.frame(subset(adonis.out.array_predGrass,
comm==comm.names.by.grass[1])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.AllG.Grass | 4 | 18.6 | 0.132 | 20.12 | 0.001 |
| Bac.AllG.Residual | 528 | 122.2 | 0.868 | ||
| Bac.AllG.Total | 532 | 140.8 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGrass,
comm==comm.names.by.grass[2])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.AllG.Grass | 4 | 13.2 | 0.064 | 8.67 | 0.001 |
| Fun.AllG.Residual | 504 | 192.5 | 0.936 | ||
| Fun.AllG.Total | 508 | 205.7 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[1])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.AllG.Bin | 3 | 7.9 | 0.056 | 10.48 | 0.001 |
| Bac.AllG.Residual | 529 | 132.9 | 0.944 | ||
| Bac.AllG.Total | 532 | 140.8 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[2])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.AllG.Bin | 3 | 11.3 | 0.055 | 9.79 | 0.001 |
| Fun.AllG.Residual | 505 | 194.4 | 0.945 | ||
| Fun.AllG.Total | 508 | 205.7 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[3])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.ANGE.Bin | 3 | 4.4 | 0.157 | 6.33 | 0.001 |
| Bac.ANGE.Residual | 102 | 23.8 | 0.843 | ||
| Bac.ANGE.Total | 105 | 28.2 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[4])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.ANGE.Bin | 3 | 6.4 | 0.174 | 6.59 | 0.001 |
| Fun.ANGE.Residual | 94 | 30.6 | 0.826 | ||
| Fun.ANGE.Total | 97 | 37.0 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[5])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.BOER.Bin | 3 | 6 | 0.354 | 14.58 | 0.001 |
| Bac.BOER.Residual | 80 | 11 | 0.646 | ||
| Bac.BOER.Total | 83 | 17 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[6])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.BOER.Bin | 3 | 5.4 | 0.194 | 5.94 | 0.001 |
| Fun.BOER.Residual | 74 | 22.3 | 0.806 | ||
| Fun.BOER.Total | 77 | 27.7 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[7])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.BOGR.Bin | 3 | 2.9 | 0.168 | 5.51 | 0.001 |
| Bac.BOGR.Residual | 82 | 14.3 | 0.832 | ||
| Bac.BOGR.Total | 85 | 17.2 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[8])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.BOGR.Bin | 3 | 4.0 | 0.126 | 4.24 | 0.001 |
| Fun.BOGR.Residual | 88 | 27.7 | 0.874 | ||
| Fun.BOGR.Total | 91 | 31.8 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[9])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.BUDA.Bin | 3 | 2.6 | 0.171 | 5.45 | 0.001 |
| Bac.BUDA.Residual | 79 | 12.4 | 0.829 | ||
| Bac.BUDA.Total | 82 | 15.0 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[10])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.BUDA.Bin | 3 | 4.3 | 0.14 | 4.11 | 0.001 |
| Fun.BUDA.Residual | 76 | 26.5 | 0.86 | ||
| Fun.BUDA.Total | 79 | 30.8 | 1.00 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[11])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.SCSC.Bin | 3 | 4.0 | 0.09 | 5.58 | 0.001 |
| Bac.SCSC.Residual | 170 | 40.8 | 0.91 | ||
| Bac.SCSC.Total | 173 | 44.8 | 1.00 |
knitr::kable((as.data.frame(subset(adonis.out.array_predBin,
comm==comm.names.by.grass[12])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.SCSC.Bin | 3 | 5.3 | 0.081 | 4.62 | 0.001 |
| Fun.SCSC.Residual | 157 | 60.0 | 0.919 | ||
| Fun.SCSC.Total | 160 | 65.3 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[1])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.AllG.Gradient | 2 | 17.5 | 0.124 | 37.57 | 0.001 |
| Bac.AllG.Residual | 530 | 123.3 | 0.876 | ||
| Bac.AllG.Total | 532 | 140.8 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[2])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.AllG.Gradient | 2 | 10.4 | 0.05 | 13.45 | 0.001 |
| Fun.AllG.Residual | 506 | 195.3 | 0.95 | ||
| Fun.AllG.Total | 508 | 205.7 | 1.00 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[3])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.ANGE.Gradient | 1 | 2.8 | 0.1 | 11.62 | 0.001 |
| Bac.ANGE.Residual | 104 | 25.4 | 0.9 | ||
| Bac.ANGE.Total | 105 | 28.2 | 1.0 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[4])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.ANGE.Gradient | 1 | 1.8 | 0.05 | 5.04 | 0.001 |
| Fun.ANGE.Residual | 96 | 35.2 | 0.95 | ||
| Fun.ANGE.Total | 97 | 37.0 | 1.00 |
[NA on stats here - only 1 factor level]
# knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
# comm==comm.names.by.grass[5])))[,1:5], "simple",
# digits =c (0,1,3,2,3),
# align = 'c')
# knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
# comm==comm.names.by.grass[6])))[,1:5], "simple",
# digits =c (0,1,3,2,3),
# align = 'c')
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[7])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.BOGR.Gradient | 2 | 2.6 | 0.152 | 7.45 | 0.001 |
| Bac.BOGR.Residual | 83 | 14.5 | 0.848 | ||
| Bac.BOGR.Total | 85 | 17.2 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[8])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.BOGR.Gradient | 2 | 3.5 | 0.109 | 5.46 | 0.001 |
| Fun.BOGR.Residual | 89 | 28.3 | 0.891 | ||
| Fun.BOGR.Total | 91 | 31.8 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[9])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.BUDA.Gradient | 2 | 2.6 | 0.172 | 8.3 | 0.001 |
| Bac.BUDA.Residual | 80 | 12.4 | 0.828 | ||
| Bac.BUDA.Total | 82 | 15.0 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[10])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.BUDA.Gradient | 2 | 3.0 | 0.097 | 4.14 | 0.001 |
| Fun.BUDA.Residual | 77 | 27.8 | 0.903 | ||
| Fun.BUDA.Total | 79 | 30.8 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[11])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Bac.SCSC.Gradient | 2 | 5.9 | 0.131 | 12.93 | 0.001 |
| Bac.SCSC.Residual | 171 | 38.9 | 0.869 | ||
| Bac.SCSC.Total | 173 | 44.8 | 1.000 |
knitr::kable((as.data.frame(subset(adonis.out.array_predGradient,
comm==comm.names.by.grass[12])))[,1:5], "simple",
digits =c (0,1,3,2,3),
align = 'c')
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Fun.SCSC.Gradient | 2 | 3.4 | 0.052 | 4.37 | 0.001 |
| Fun.SCSC.Residual | 158 | 61.8 | 0.948 | ||
| Fun.SCSC.Total | 160 | 65.3 | 1.000 |